Outsourcing mundane tasks and jobs to online labor around the globe has demonstrated potential in assisting entities and individuals complete a large amount of tasks in a time efficient manner. However, given the variability in labor pool, education level, age group, the results of the completed tasks are frequently less than ideal. Although entities could utilize crowd sourcing to have tasks completed in a short amount of time for a reasonable cost, the quality is typically highly variable and frequently unreliable.
Crowd sourcing is a growing industry, and online platforms exist for completion of tasks and microtasks. Labor-on-demand platforms provide APIs for application development and an international payment infrastructure for the deployment and implementation of crowd sourced tasks. Crowd sourcing has become a global phenomenon with a low barrier to entry. Millions of people around the world now participate in a digital and virtual workplace. However, the breadth and diversity among crowd sourcing participants, workers and researchers is immense and poses a challenge to implement adequate quality control mechanisms to account for a wide range of worker behavior: scammers, lazy workers, ethical workers, unethical workers, slow learners, etc. in a resource effective manner.